Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK.

ABSTRACT: During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022-23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. Forecast uncertainty was modelled usings a novel generalised additive model f

SUBMITTER: Morbey RA 

PROVIDER: S-EPMC11771935 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature